After 50 years and countless dead ends, incremental progress, and modest breakthroughs, artificial intelligence researchers are asking for a do-over.

The $5 million Mind Machine Project (MMP), a patchwork team of two dozen academics, students and researchers, intends to go back to the discipline's beginnings, rebuilding the field from the ground up. With 20/20 hindsight, a few generations worth of experience, and better, faster technology, this time researchers in AI — an ambiguous field to begin with — plan to get things right.

The study of AI is a half a century old, beginning with lofty expectations at a 1956 conference but quickly fragmenting into different specializations and sub-fields. The MMP wants to roll back the clock, fixing early assumptions that are now foundations of the field and redefining what the objectives of AI research should be.

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The fundamental problem, it seems, is that the mind, memory and body function both together and separately to solve any number of problems, and the way they work together (and alone) varies from problem to problem. The human mind alone applies various systems and functions to any given problem. Many AI solutions have attempted to solve all the problems with one system or function rather than multiple systems working together as in the human mind, a "silver bullet" approach that hinders real progress.

Likewise, when it comes to memory, researchers have created models that work more like computers, where everything is either one or zero. Real memory is filled with gray areas, ambiguities and inconsistencies, but functions in spite of not always being congruent. MMP researchers also intend to bring computer science and physiology together, forcing computers to work within the confines of physical space and time just like the body does.

The team even proposes discarding the Turing Test, the long-recognized standard for determining artificial intelligence. Instead, MMP researchers want to test for a machine's comprehension of a children's book — rather than a human's comprehension of another human being — to gain a better understanding or the AI's ability to process and regurgitate thought.

It's a big-picture approach to a big challenge, and while it's perhaps unlikely that the team can re-imagine AI in the ambitious five-year window they've given themselves, it very well could shore up some of the loose underpinnings of a discipline that has boundless potential to shape a better world (or, for you SkyNet junkies, limitless potential to destroy it). If nothing else, it's a responsible admission from the scientific community that they simply don't have it quite right, that we need to rethink what we think we know.